Minimum information about a microarray experiment (MIAME)-toward standards for microarray data.

Microarray analysis has become a widely used tool for the generation of gene expression data on a genomic scale. Although many significant results have been derived from microarray studies, one limitation has been the lack of standards for presenting and exchanging such data. Here we present a proposal, the Minimum Information About a Microarray Experiment (MIAME), that describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of this work is to establish a standard for recording and reporting microarray-based gene expression data, which will in turn facilitate the establishment of databases and public repositories and enable the development of data analysis tools. With respect to MIAME, we concentrate on defining the content and structure of the necessary information rather than the technical format for capturing it.

Aligning gene expression time series with time warping algorithms.

UNLABELLED: motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another. RESULTS: We present implementations of time warping algorithms applicable to RNA and protein expression data and demonstrate their application to published yeast RNA expression time series. Programs executing two warping algorithms are described, a simple warping algorithm and an interpolative algorithm, along with programs that generate graphics that visually present alignment information. We show time warping to be superior to simple clustering at mapping corresponding time states. We document the impact of statistical measurement noise and sample size on the quality of time alignments, and present issues related to statistical assessment of alignment quality through alignment scores. We also discuss directions for algorithm improvement including development of multiple time series alignments and possible applications to causality searches and non-temporal processes ('concentration warping').

Computational comparison of two draft sequences of the human genome.

We are in the enviable position of having two distinct drafts of the human genome sequence. Although gaps, errors, redundancy and incomplete annotation mean that individually each falls short of the ideal, many of these problems can be assessed by comparison. Here we present some comparative analyses of these drafts. We look at a number of features of the sequences, including sequence gaps, continuity, consistency between the two sequences and patterns of DNA-binding protein motifs.

Systematic management and analysis of yeast gene expression data.

We report steps toward the systematic management, standardization, and analysis of functional genomics data. We developed the ExpressDB database for yeast RNA expression data and loaded it with approximately 17.5 million pieces of data reported by 11 studies with three different kinds of high-throughput RNA assays. A web-based tool supports queries across the data from these studies. We examined comparability of data by converting data from 9 studies (217 conditions) into mRNA relative abundance estimates (ERAs) and by clustering of conditions by ERAs. We report on generation of ERAs and condition clustering for non-microarray data (5 studies, 63 conditions) and describe initial attempts to generate microarray-based ERAs (4 studies, 154 conditions), which exhibit increased error, on our web site http://arep.med.harvard. edu/ExpressDB. We recommend standards for data reporting, suggest research into improving comparability of microarray data through quantifying and standardizing control condition RNA populations, and also suggest research into the calibration of different RNA assays. We introduce a model for a database that integrates different kinds of functional genomics data, Biomolecule Interaction, Growth and Expression Database (BIGED).